Machine Learning-Enabled Battery Management System on FPGA for Electric Vehicles

This study presents an FPGA-based implementation of an adaptive power management unit (APMU) for electric vehicles (EVs), leveraging a hybrid bidirectional long short-term memory (Bi-LSTM) network and decision tree classifier to optimize power distribution in real time. Developed on the Zynq UltraSc...

Full description

Saved in:
Bibliographic Details
Published in:Programming and computer software Vol. 51; no. 6; pp. 373 - 384
Main Authors: Daisy Merina. R, Saravana Ram. R, Lordwin Cecil Prabhaker Micheal
Format: Journal Article
Language:English
Published: Moscow Pleiades Publishing 01.12.2025
Springer Nature B.V
Subjects:
Accuracy
Artificial Intelligence
Batteries
Computer Science
Cost control
Decision trees
Driving conditions
Electric vehicles
Emissions
Energy consumption
Energy efficiency
Energy management
Field programmable gate arrays
Genetic algorithms
Lithium
Machine learning
Neural networks
Operating Systems
Power efficiency
Power management
Software Engineering
Software Engineering/Programming and Operating Systems
State of charge
Traffic speed
driving pattern recognition using machine learning
battery management system (BMS) for electric vehicles
long short-term memory (LSTM) networks
bidirectional LSTM (Bi-LSTM) architecture
decision tree classification
state of charge (SoC) estimation algorithms
FPGA-based embedded implementation
ISSN:0361-7688, 1608-3261
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This study presents an FPGA-based implementation of an adaptive power management unit (APMU) for electric vehicles (EVs), leveraging a hybrid bidirectional long short-term memory (Bi-LSTM) network and decision tree classifier to optimize power distribution in real time. Developed on the Zynq UltraScale+ MPSoC platform, the proposed system estimates the state of charge (SoC) and classifies driving conditions to dynamically allocate power to onboard components. The FPGA testbed models a mid-range EV by simulating key parameters such as throttle position, vehicle speed, battery voltage/current, and GPS data at 30-second intervals. Experimental results demonstrate significant improvements in power efficiency and computational latency compared to conventional battery management units (BMUs). The proposed system consumes approximately 0.98 W, achieves a latency of 5.6 µs, and operates at 181.6 operations per watt-far surpassing traditional microcontroller or DSP-based BMUs. Range estimation shows up to a 25% increase under highway conditions using the Bi-LSTM + decision tree model, validating the effectiveness of the adaptive strategy for intelligent energy management in EVs.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0361-7688
1608-3261
DOI:10.1134/S0361768825700240